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題名 探討偏好啟發對微時刻推薦的影響：互動式微時刻推薦系統
The influence of preference elicitation to micro-moment recommendations: An interactive MMRS
作者 王詩堯
Wang, Shih-Yao
貢獻者 林怡伶
Lin, Yi-Ling
王詩堯
Wang, Shih-Yao
關鍵詞 微時刻
推薦系統
意圖
偏好啟發
互動式設計
聊天機器人
Micro-moments
Recommendation system
Intention
Preference elicitation
Interactive design
Chatbot
日期 2020
上傳時間 2-Sep-2020 11:48:05 (UTC+8)
摘要 先前的研究指出推薦系統不僅應根據用戶的行為數據或受歡迎的項目進行推薦，還應符合用戶的偏好。部分推薦系統設計在使用者首次加入時調查其長期偏好。然而當在微時刻情境下，必須在限時內做出決策的壓力會導致注意力的限縮，最終會因此做出跟平時不同的選擇。這使我們相信作為決策輔助的推薦系統也應考慮短期意圖，並透過與使用者的互動來捕捉。這項研究進行了為期三週的使用者研究，以根據熱門程度、長期偏好和短期意圖來比較推薦的效果。本實驗設計了三個階段，包括進入前調查、使用聊天機器人、實驗後調查和訪談。總共招募了120名大學生，並將他們平均分配到四組之中。實驗的主要任務為透過與聊天機器人進行互動，在微時刻的各種情境下選擇一間餐廳。實驗結果顯示，MIX組（同時考慮長期偏好和短期意圖）會話的成功率比LTP組（僅捕獲長期偏好）高21.8%，並且利用更少的動作完成一輪推薦流程。另外，MIX組的所選項目在推薦列表上的平均排名最低，且推薦的點擊率最高。結果證明，這是四組中能支持使用者以較少的努力做出有效決策的最佳設計，而且該設計也是最適合支持微時刻的情境。透過證明MIX組優於LTP組，證明了在微時刻捕捉短期意圖的重要性。
Previous studies pointed out that recommendation systems should not only recommend by user`s behavioral data or popular items but should conform to user preferences. Some recommendation systems investigate users’ long-term preferences when they first join. However, in micro-moments, giving limited available time to make decisions leads to a narrowing of attentional focus, eventually comes up with different choices. It convinces us that short-term intentions should also be taken into consideration and obtained through interactions with users. This research conducts a three-week user study to compare the effects of recommendations based on popularity, long-term preferences, and short-term intentions. Three phases including onboarding survey, chatbot use, post-experiment survey and interview were designed in this experiment. A total of 120 university students were recruited and assigned to one out of four groups. The main tasks focused on interacting with the chatbot then making choices of restaurants under various situations of micro-moments. The result shows that the sessions of the MIX group (considering both long-term preferences and short-term intentions) have a more 21.8% success ratio than the LTP group ones (capturing only the long-term preferences) and spent fewer actions in the recommendation processes. In addition, the mean of the MIX group` s selected position is the lowest, and the click-through of the MIX group is the highest. The results proved that it is the best design among four groups supporting users to make effective decisions with fewer efforts, moreover, this design is most suitable for the situation under micro-moments. Comparing the design of the LTP group, it also shows the importance of capturing short-term intentions at micro-moments.
參考文獻 Amatriain, X., Pujol, J. M., & Oliver, N. (2009). I like it... I like it not: Evaluating user ratings noise in recommender systems. In International Conference on User Modeling, Adaptation, and Personalization (pp. 247–258). Springer.
Ariely, D. (2016). Time pressure: Behavioral science considerations for mobile marketing.
Baltrunas, L., Kaminskas, M., Ludwig, B., Moling, O., Ricci, F., Aydin, A., & Schwaiger, R. (2011). Incarmusic: Context-aware music recommendations in a car. In International Conference on Electronic Commerce and Web Technologies, 89–100.
Chen, J. W. (2016). A study on the selection of fast food restaurant by Utar Kampar students using analytic hierarchy process (AHP). Doctoral Dissertation, UTAR.
Chen, L., & Pu, P. (2007a). Hybrid critiquing-based recommender systems, 22–31.
Chen, L., & Pu, P. (2007b). Preference-based organization interfaces: Aiding user critiques in recommender systems. In International Conference on User Modeling (pp. 77–86). Springer.
Chen, L., & Pu, P. (2012). Critiquing-based recommenders: Survey and emerging trends. User Modeling and User-Adapted Interaction, 22(1–2), 125–150.
Chernev, A. (2003). When more is less and less is more: The role of ideal point a vailability and assortment in consumer choice. Journal of Consumer Research, 30(2), 170–183.
Christakopoulou, K., Radlinski, F., & Hofmann, K. (2016). Towards conversational recommender systems. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-Augu(3), 815–824.
Coombs, L. C. (1974). The measurement of family size preferences and subsequent fertility. Demography, 11(4), 587–611.
Costa, H., Furtado, B., Pires, D., Macedo, L., & Cardoso, A. (2012). Context and intention-awareness in POIs recommender systems. CEUR Workshop Proceedings, 889, 1–5.
Dali Betzalel, N., Shapira, B., & Rokach, L. (2015). “Please, not now!” A model for timing recommendations. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 297–300).
DelCarmen Rodríguez-Hernández, M., & Ilarri, S. (2014). Towards a context-aware mobile recommendation architecture. International Conference on Mobile Web and Information Systems, 56–70.
Ekstrand, M. D., & Willemsen, M. C. (2016). Behaviorism is not enough: Better recommendations through listening to users. RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems, 221–224.
Esmeli, R., Bader-El-Den, M., & Mohasseb, A. (2019). Context and short term user intention aware hybrid session based recommendation system. IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings, 1–6.
Ghose, A., Han, S. P., & Xu, K. (2013). Mobile commerce in the new tablet economy. International Conference on Information Systems (ICIS 2013): Reshaping Society Through Information Systems Design, 3, 2591–2608.
Han, J., & Yamana, H. (2017). A survey on recommendation methods beyond accuracy. IEICE TRANSACTIONS on Information and Systems, 100(12), 2931–2944.
Hendrianto, A. (2017). Analysis of students preferences in choosing restaurant around campus area.
Inzunza, S., Juárez-Ramírez, R., Jiménez, S., & Licea, G. (2018). GUMCARS: General user model for context-aware recommender systems, 37, 1149–1183.
Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: Can one desire too much of a good thing? Journal of Personality and Social Psychology, 79(6), 995–1006.
Jannach, D., Resnick, P., Tuzhilin, A., & Zanker, M. (2016). Recommender systems-beyond matrix completion. Communications of the ACM, 59(11), 94–102.
Jin, Y., Cai, W., Chen, L., Htun, N. N., & Verbert, K. (2019). MusicBot: Evaluating critiquing-based music recommenders with conversational interaction. International Conference on Information and Knowledge Management, Proceedings, 951–960.
Jugovac, M., Jannach, D., & Dortmund, T. (2017). Interacting with recommenders—overview and research. ACM Transactions on Interactive Intelligent Systems (TiiS), 7(3), 10.
Kaminskas, M., & Bridge, D. (2016). Diversity, serendipity, novelty, and coverage: A survey and empirical analysis of beyond-Accuracy objectives in recommender systems. ACM Transactions on Interactive Intelligent Systems, 7(1), 1–42.
Kilinc, C. C., Semiz, M., Katircioglu, E., & Unusan, Ç. (2013). Choosing restaurant for lunch in campus area by the compromise decision via AHP. International Journal of Economic Perspectives, 7(2).
Knijnenburg, B. P., Reijmer, N. J. M., & Willemsen, M. C. (2011). Each to his own: how different users call for different interaction methods in recommender systems. In Proceedings of the fifth ACM conference on Recommender systems (pp. 141–148).
Knijnenburg, B. P., Sivakumar, S., & Wilkinson, D. (2016). Recommender systems for self-actualization. RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems, 11–14.
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描述 碩士
國立政治大學
資訊管理學系
107356034
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107356034
資料類型 thesis
dc.contributor.advisor 林怡伶zh_TW
dc.contributor.advisor Lin, Yi-Lingen_US
dc.contributor.author (Authors) 王詩堯zh_TW
dc.contributor.author (Authors) Wang, Shih-Yaoen_US
dc.creator (作者) 王詩堯zh_TW
dc.creator (作者) Wang, Shih-Yaoen_US
dc.date (日期) 2020en_US
dc.date.accessioned 2-Sep-2020 11:48:05 (UTC+8)-
dc.date.available 2-Sep-2020 11:48:05 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2020 11:48:05 (UTC+8)-
dc.identifier (Other Identifiers) G0107356034en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131501-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 107356034zh_TW
dc.description.abstract (摘要) 先前的研究指出推薦系統不僅應根據用戶的行為數據或受歡迎的項目進行推薦，還應符合用戶的偏好。部分推薦系統設計在使用者首次加入時調查其長期偏好。然而當在微時刻情境下，必須在限時內做出決策的壓力會導致注意力的限縮，最終會因此做出跟平時不同的選擇。這使我們相信作為決策輔助的推薦系統也應考慮短期意圖，並透過與使用者的互動來捕捉。這項研究進行了為期三週的使用者研究，以根據熱門程度、長期偏好和短期意圖來比較推薦的效果。本實驗設計了三個階段，包括進入前調查、使用聊天機器人、實驗後調查和訪談。總共招募了120名大學生，並將他們平均分配到四組之中。實驗的主要任務為透過與聊天機器人進行互動，在微時刻的各種情境下選擇一間餐廳。實驗結果顯示，MIX組（同時考慮長期偏好和短期意圖）會話的成功率比LTP組（僅捕獲長期偏好）高21.8%，並且利用更少的動作完成一輪推薦流程。另外，MIX組的所選項目在推薦列表上的平均排名最低，且推薦的點擊率最高。結果證明，這是四組中能支持使用者以較少的努力做出有效決策的最佳設計，而且該設計也是最適合支持微時刻的情境。透過證明MIX組優於LTP組，證明了在微時刻捕捉短期意圖的重要性。zh_TW
dc.description.abstract (摘要) Previous studies pointed out that recommendation systems should not only recommend by user`s behavioral data or popular items but should conform to user preferences. Some recommendation systems investigate users’ long-term preferences when they first join. However, in micro-moments, giving limited available time to make decisions leads to a narrowing of attentional focus, eventually comes up with different choices. It convinces us that short-term intentions should also be taken into consideration and obtained through interactions with users. This research conducts a three-week user study to compare the effects of recommendations based on popularity, long-term preferences, and short-term intentions. Three phases including onboarding survey, chatbot use, post-experiment survey and interview were designed in this experiment. A total of 120 university students were recruited and assigned to one out of four groups. The main tasks focused on interacting with the chatbot then making choices of restaurants under various situations of micro-moments. The result shows that the sessions of the MIX group (considering both long-term preferences and short-term intentions) have a more 21.8% success ratio than the LTP group ones (capturing only the long-term preferences) and spent fewer actions in the recommendation processes. In addition, the mean of the MIX group` s selected position is the lowest, and the click-through of the MIX group is the highest. The results proved that it is the best design among four groups supporting users to make effective decisions with fewer efforts, moreover, this design is most suitable for the situation under micro-moments. Comparing the design of the LTP group, it also shows the importance of capturing short-term intentions at micro-moments.en_US
dc.description.tableofcontents Chapter 1 INTRODUCTION 1
1.1 Background and motivation 1
1.2 Research method 2
1.3 Research questions 3
Chapter 2 LITERATURE REVIEW 4
2.1 Problem derived from micro-moments 4
2.2 Intent research gap 6
2.3 Interactive recommendation systems 7
2.4 Preference elicitation 8
Chapter 3 RESEARCH METHODOLOGY 9
3.1 Dataset 9
3.2 Chatbot system 11
3.3 Tasks 15
3.4 Participants 16
3.5 Design 17
3.5.1 Phase 1: Onboarding survey 18
3.5.2 Phase 2: Chatbot use 21
3.5.3 Phase 3: Post-experiment survey and interview 24
3.6 Procedure 24
3.7 Hypotheses 25
Chapter 4 ANALYSIS AND RESULTS 26
4.1 Analysis of onboarding survey 26
4.1.1 AHP test: seven criteria 26
4.1.2 Food type form: nine cuisines 28
4.2 Analysis of chatbot log 29
4.2.1 RQ1: Decision quality 31
4.2.2 RQ2: Perceived effort 43
4.2.3 RQ3: User perception 47
Chapter 5 CONCLUSION 52
5.1 Discussions and limitations 52
5.1.1 Discussions of research questions 53
5.1.2 Discussions of user interview 55
5.2 Theoretical and practical contributions 56
5.2.1 Theoretical contributions 56
5.2.2 Practical contributions 57
5.3 Conclusion 58
REFERENCE 61
APPENDIX 1 – AHP test 67
APPENDIX 2 – Post-experiment survey 70		zh_TW
dc.format.extent 2660402 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107356034en_US
dc.subject (關鍵詞) 微時刻zh_TW
dc.subject (關鍵詞) 推薦系統zh_TW
dc.subject (關鍵詞) 意圖zh_TW
dc.subject (關鍵詞) 偏好啟發zh_TW
dc.subject (關鍵詞) 互動式設計zh_TW
dc.subject (關鍵詞) 聊天機器人zh_TW
dc.subject (關鍵詞) Micro-momentsen_US
dc.subject (關鍵詞) Recommendation systemen_US
dc.subject (關鍵詞) Intentionen_US
dc.subject (關鍵詞) Preference elicitationen_US
dc.subject (關鍵詞) Interactive designen_US
dc.subject (關鍵詞) Chatboten_US
dc.title (題名) 探討偏好啟發對微時刻推薦的影響：互動式微時刻推薦系統zh_TW
dc.title (題名) The influence of preference elicitation to micro-moment recommendations: An interactive MMRSen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Amatriain, X., Pujol, J. M., & Oliver, N. (2009). I like it... I like it not: Evaluating user ratings noise in recommender systems. In International Conference on User Modeling, Adaptation, and Personalization (pp. 247–258). Springer.
Ariely, D. (2016). Time pressure: Behavioral science considerations for mobile marketing.
Baltrunas, L., Kaminskas, M., Ludwig, B., Moling, O., Ricci, F., Aydin, A., & Schwaiger, R. (2011). Incarmusic: Context-aware music recommendations in a car. In International Conference on Electronic Commerce and Web Technologies, 89–100.
Chen, J. W. (2016). A study on the selection of fast food restaurant by Utar Kampar students using analytic hierarchy process (AHP). Doctoral Dissertation, UTAR.
Chen, L., & Pu, P. (2007a). Hybrid critiquing-based recommender systems, 22–31.
Chen, L., & Pu, P. (2007b). Preference-based organization interfaces: Aiding user critiques in recommender systems. In International Conference on User Modeling (pp. 77–86). Springer.
Chen, L., & Pu, P. (2012). Critiquing-based recommenders: Survey and emerging trends. User Modeling and User-Adapted Interaction, 22(1–2), 125–150.
Chernev, A. (2003). When more is less and less is more: The role of ideal point a vailability and assortment in consumer choice. Journal of Consumer Research, 30(2), 170–183.
Christakopoulou, K., Radlinski, F., & Hofmann, K. (2016). Towards conversational recommender systems. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-Augu(3), 815–824.
Coombs, L. C. (1974). The measurement of family size preferences and subsequent fertility. Demography, 11(4), 587–611.
Costa, H., Furtado, B., Pires, D., Macedo, L., & Cardoso, A. (2012). Context and intention-awareness in POIs recommender systems. CEUR Workshop Proceedings, 889, 1–5.
Dali Betzalel, N., Shapira, B., & Rokach, L. (2015). “Please, not now!” A model for timing recommendations. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 297–300).
DelCarmen Rodríguez-Hernández, M., & Ilarri, S. (2014). Towards a context-aware mobile recommendation architecture. International Conference on Mobile Web and Information Systems, 56–70.
Ekstrand, M. D., & Willemsen, M. C. (2016). Behaviorism is not enough: Better recommendations through listening to users. RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems, 221–224.
Esmeli, R., Bader-El-Den, M., & Mohasseb, A. (2019). Context and short term user intention aware hybrid session based recommendation system. IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings, 1–6.
Ghose, A., Han, S. P., & Xu, K. (2013). Mobile commerce in the new tablet economy. International Conference on Information Systems (ICIS 2013): Reshaping Society Through Information Systems Design, 3, 2591–2608.
Han, J., & Yamana, H. (2017). A survey on recommendation methods beyond accuracy. IEICE TRANSACTIONS on Information and Systems, 100(12), 2931–2944.
Hendrianto, A. (2017). Analysis of students preferences in choosing restaurant around campus area.
Inzunza, S., Juárez-Ramírez, R., Jiménez, S., & Licea, G. (2018). GUMCARS: General user model for context-aware recommender systems, 37, 1149–1183.
Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: Can one desire too much of a good thing? Journal of Personality and Social Psychology, 79(6), 995–1006.
Jannach, D., Resnick, P., Tuzhilin, A., & Zanker, M. (2016). Recommender systems-beyond matrix completion. Communications of the ACM, 59(11), 94–102.
Jin, Y., Cai, W., Chen, L., Htun, N. N., & Verbert, K. (2019). MusicBot: Evaluating critiquing-based music recommenders with conversational interaction. International Conference on Information and Knowledge Management, Proceedings, 951–960.
Jugovac, M., Jannach, D., & Dortmund, T. (2017). Interacting with recommenders—overview and research. ACM Transactions on Interactive Intelligent Systems (TiiS), 7(3), 10.
Kaminskas, M., & Bridge, D. (2016). Diversity, serendipity, novelty, and coverage: A survey and empirical analysis of beyond-Accuracy objectives in recommender systems. ACM Transactions on Interactive Intelligent Systems, 7(1), 1–42.
Kilinc, C. C., Semiz, M., Katircioglu, E., & Unusan, Ç. (2013). Choosing restaurant for lunch in campus area by the compromise decision via AHP. International Journal of Economic Perspectives, 7(2).
Knijnenburg, B. P., Reijmer, N. J. M., & Willemsen, M. C. (2011). Each to his own: how different users call for different interaction methods in recommender systems. In Proceedings of the fifth ACM conference on Recommender systems (pp. 141–148).
Knijnenburg, B. P., Sivakumar, S., & Wilkinson, D. (2016). Recommender systems for self-actualization. RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems, 11–14.
Lai, J. Y., Debbarma, S., & Ulhas, K. R. (2012). An empirical study of consumer switching behaviour towards mobile shopping: A Push-Pull-Mooring model. International Journal of Mobile Communications, 10(4), 386–404.
Levene, H. (1960). Contributions to probability and statistics. Essays in Honor of Harold Hotelling, 278–292.
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dc.identifier.doi (DOI) 10.6814/NCCU202001650en_US